The former is a platform for creating predictive APIs hosted on the Microsoft Azure cloud, whereas the latter is an open source machine learning server that you run on your own infrastructure, also to expose predictive models as APIs.

It’s clear that both companies have similar visions and similar features, but when digging deeper you’ll notice key differences and key advantages to each...

Similar visions One major hurdle that companies encounter in their machine learning projects is taking data scientists’ work to production in order to deliver predictions to end users (who’ll ultimately benefit from them).

Another aspect on which both organizations have been working on is the ability to create and reuse predefined templates and workflows to help their users launch predictive APIs more quickly than with traditional development methods.

Azure’s strengths Although PredictionIO’s install is super easy (just a one-line command, or you can fire up an already provisioned Amazon instance or a Terminal.com snap in 5 seconds), with Azure there’s nothing to install at all.

Azure ML’s interface with its canvas (in the middle) One advantage of working with a cloud platform such as Azure is its auto-scaling feature: models are deployed in a way that’s elastic and you don’t have to worry about scaling out your APIs.

On Thursday, March 21, 2019

Designing Predictive Algorithms for Machine Learning

In 2014, Machine learning was one of the newest and most utilizable tools in a Data Scientist's arsenal. In this TechEd talk you will learn key architectural ...

PredictionIO:­ Building Smarter Apps With Machine Learning - Time Series (Part 1 of 3)